Analysis And Design Of Bank Counter Intelligent Marketing System

In the banking sector, with the deepening of the financial information and interest rate marketization, and the Internet financ ial impact to the traditional financial institutions, banking f inancial institutions face enormous pressure of competition. However, the nature of competition between Banks is customer resources competition. Under the situation of product hom ogenization in the bank, custom er resources competition is to g et a first look at cus tomers and to m eet the financial needs of customers. The use of data mining technology, to capture the real needs of custom ers in the vast am ounts of data, provides a scientif ic method for the classification of banking customer.This paper first expounds the research situ ation of data m ining technology in the financial industry at hom e and abroad and its application in the banking sector. At present, although m ost of the data m ining systems are used to support the decision-making for managem ents or the an alysis of custom er and m arket of marketing department, there is few specifi c application for customer managers and counter staffs in basic-level banks. Speci ally, although the sm all and medium-sized financial institutions, e.g., city co mmercial banks, rural commercial banks, rural credit cooperatives, set up the data ware house, there a re still lack ing in data processing. According to the survey, there has not been a m arketing system for frontline customer managers and counters. Then, clustering algorithms of data mining technology are analyzed, and the principle of clustering analysis algorithm s and the applications of K-Means algorithm also been introduced in this paper,This article gives the example of the rural commercial banks in N city. By using the method of software engineering, the requirements analysis and design of system have been done. Then, based on custom er segmentation and considering the existing information(e.g., basic customer info rmation, asset inform ation, financial information and details of transaction), the clustering analysis of data mining method is used to a nalysis the data, which comes from the data platform of F provincial association and third-party tr ansactions. Hence, the characteristics of customers are classified, and the features of dif ferent customer groups are summ arized. Finally, according to these features, the ap propriate marketing strategies are selected an d pushed to the teller to display by the data analysis system. In this way, decision-making information is provided to counters for service marketing.At the end of this paper, the problems, which are caused in the process of analysis and design of the intelligent marketing system for the bank counter, are analyzed, and the expansion of the follow-up to the system and im provement of this work is prospected.